Deep Stack Neural Networks Based Learning Model for Fault Detection and Classification in Sensor Data

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

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Fault detection and classification approaches in transmission lines using artificial neural networks

MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference ◽

10.1109/melcon.2014.6820588 ◽

2014 ◽

Cited By ~ 14

Author(s):

Moez Ben Hessine ◽

Houda Jouini ◽

Souad Chebbi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fault Detection ◽

Transmission Lines ◽

Fault Detection And Classification ◽

Artificial Neural

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Fault detection and classification in Industrial IoT in case of missing sensor data

10.36227/techrxiv.14540061.v1 ◽

2021 ◽

Author(s):

Merim Dzaferagic ◽

Nicola Marchetti ◽

Irene Macaluso

Keyword(s):

Fault Detection ◽

Monitoring System ◽

Sensor Data ◽

Generative Adversarial Networks ◽

Missing Measurements ◽

Tennessee Eastman Process ◽

Fault Detection And Classification ◽

Industrial Iot ◽

Critical Components ◽

The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

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